An Integer Linear Programming Model for Earth Observation Missions

TL;DR

This paper presents an integer linear programming model for optimizing satellite earth observation missions, incorporating decentralized decision-making and dynamic communication constraints to improve task scheduling efficiency.

Contribution

It introduces a novel distributed Lagrangian relaxation approach combined with integer programming for satellite mission planning under evolving communication networks.

Findings

Demonstrates the feasibility of the model through numerical simulations.

Shows improved scheduling efficiency in dynamic communication environments.

Validates the approach for complex satellite operations.

Abstract

This paper addresses an optimization problem in satellite observation mission planning, focusing on the challenges of decentralized decision-making among satellites, which is crucial for optimizing strategies in dynamic observation environments. The method integrates mathematical modeling using integer programming and time-varying communication graphs, which are essential for efficient task scheduling. Specifically, the approach utilizes distributed Lagrangian relaxation techniques to manage the complexity of the problem. Numerical simulations are conducted to explore the feasibility of the proposed approach for handling complex satellite operations under evolving communication dynamics.